The characterization of the structure of a biomolecule on the its chemical and spectral properties is of widespread importance in the biomedical sciences. Because of its complexity, structure characterization is overall a time-consuming activity that has been largely left to experts in the field. The goal of this research is to substantially increase the productivity of those experts. Given the expert's proficiency in readily picking out the correct structure of an unknown from among a limited number of alternative structures, a much more expeditious assignment of structure would be achieved if the expert could begin the structure characterization problem with such a conceptually manageable number of plausible alternatives. Thus, the goal of the research is to create artificial intelligence based computer software capable of directly reducing the collective spectral properties (infrared, proton and carbon-13 nuclear magnetic resonance, ultraviolet and mass spectral data) of the unknown to a conceptually manageable number of stereochemically defined structures, each of which is compatible with the observed spectral data. The expert is still a required player, but is spared what is actually the time-intensive component of the "manual" process, that of producing the limited number of alternatives. Key features of the software to be created include, (1) programs for both spectrum interpretation and structure generation, (2) provision to accept any structural information known to the expert and (3) the capability to treat real-world structure characterization problems without regard to the class of structures to which the unknown belongs. The last named feature requires a system that is not spectral library dependent.